The “data-augmented super-worker that can produce more output” than you!

Following the 2017 Consumer Electronics Show (CES) I found myself speculating on the impacts to come from this year’s products and ideas. Who will be affected, which industries will implement them, and how does an organisation incorporate exponential disruptive change?

This got me thinking about the notion of a “data-augmented super-worker that can produce more output than ever before” from Brad Andrews’ blog post.

In December 2016, the Governor of the Bank of England predicted the loss of 15 million British jobs to robots and machine learning over the coming decades. Similar statements echoing around the globe make it hard not to look around and ask how many individuals/organisations are informed and forward looking when it comes to these tectonic technological shifts?

2016 saw a step change in Artificial Intelligence (AI) with the likes of the hedge funds (Bridgewater Associates) and in the automotive industry (Volkswagen) moving to implement AI off the shop floor and into corporate functions. The move is clear and simple: “To save time and eliminate human emotional volatility” Bridgewater Associates have set the target of automating “three-quarters of management decisions within five years”, and VW within seven years. Is this move bold or imperative? Only time will tell. Nevertheless, scrutiny of this type of landscape shift should already be sweeping the corridors of organisations big and small.

The consideration of single technologies may be viewed as impressively disruptive, but the impact of combining digital technologies has an exponentially compounding effect. This is especially apparent when considering the effects of advancements in cloud computing, Virtual Reality (VR) and Augmented Reality (AR). One example of this is how AR will be used for visualising design and aiding construction. Another is the learning methods of AI in autonomous vehicles. Not only is the AI “deep learning” on the roads, but by using advancements in VR, AI is being exposed and tested in simulated scenarios of weather, congestion, accidents etc. All from various angles and approaches. It’s a scalable mechanism that can extend unknown variables into the learnings. Could this logic work in your world?

These technologies are mind-blowing, but could be paled into insignificance with the momentum of distributed ledgers, specifically Blockchain (the technology underpinning cryptocurrency Bitcoin). This is a disruptor of tremendous potential.

So what is Blockchain technology? It’s difficult to summarise in a few hundred words (extra reading is mandatory), however here’s my attempt:

A hyper secure register (ledger) containing near real-time updated data that simultaneously reflects a full history of activity.

Essentially the technology is attractive as its intent is to build a foundation of trust for whatever application it’s applied to. This is accomplished by delivering confidence in three critical areas,

Banking – the ASX is deliberating use of this technology to underlie the post trade process (decision by Q4 2017)

Agriculture – in late December 2016 Australian wheat farmers used Blockchain technology to “seamlessly manage contracts, deliveries, invoices, payments, and inventory” (essentially the whole supply chain, giving full confidence to all involved in the transactions).

Mining – in September 2016 BHP Billiton said it would use this technology in its supply chain, allowing seamless workings with vendors.

Energy – A U.K. start-up is looking to move Energy suppliers to Blockchain technology claiming “such a platform would enable energy supplier switches to be executed up to 20x faster than current rates”.

I believe Blockchain has huge potential to decentralise big organisations and if adopted early will bring significant benefit.

So where’s the super-worker?

Taking a theoretical scenario of an engineering design project and combining elements of the above mentioned technology, we may have an idea:

Data – All data required for decision making (e.g. geospatial data, vendor data and client specified parameter etc.) will be held on Blockchain technology

Execution – The design will be automated based on learned rules and principles, with AI interacting directly with the data held on the Blockchain

Resources – Just add people for further creativity and QA.

Enter the super workforce!

This scenario essentially translates to seamless, unambiguous, near real-time, unbiased decision making – with a significant cost, operational efficiency and safety implication to all. Oh, and it’s scalable!

So when?

Considering the ambitious schedules outlined above and predictions for the hugely topical autonomous car industry: Ford and BMW are targeting fully automated driverless vehicles by 2021, Tesla by 2023, and Uber a full driverless fleet by 2030 – All very much within our lifetime.

The tide is rising with more disruptive technologies arriving every day and each holding the potential to bring another round of evolutionary shifts, we are truly in exponentially changing digital times!

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David Johnson is a principal consultant with Advisian. He has 12 Years of technical, consultancy and project delivery experience which has driven his enthusiasm for technological advancements and the limitless opportunities they present. He holds a BSc (Hons) in Remote Sensing and Geographic Information Systems from Bath Spa University.